Diffusion Models for Radio Map Estimation: Theoretical Performance Analysis and Sampling Rate Guideline
This work uses diffusion models to estimate radio maps from sparse sensor data when ground truth is unavailable. It frames the problem as non-linear matrix completion, deriving theoretical error bounds and empirical approximations. The findings provide guidelines for optimal sensor sampling rates and training strategies, enabling performance evaluation in cost-sensitive scenarios.